System and method for controlling an autonomous vehicle

ABSTRACT

A method, a system, and non-transitory computer readable medium for controlling an autonomous vehicle are provided. The method includes identifying a trend for an autonomous vehicle based on autonomous vehicle profiles associated with one or more vehicles within a network, identifying optimal driving conditions for the autonomous vehicle based on the trend; and controlling one or more subsystems of the autonomous vehicle based on the identified optimal driving conditions.

BACKGROUND

Cloud computing techniques may be used by vehicle manufacturers tocontrol the flow of autonomous vehicles. U.S. Pat. No. 9,566,983 B2entitled “Control arrangement arranged to control an autonomous vehicle,autonomous drive arrangement, vehicle and method” by Harda describes amethod to control the velocity of an autonomous vehicle based on apreceding vehicle velocity.

The foregoing “Background” description is for the purpose of generallypresenting the context of the disclosure. Work of the inventor, to theextent it is described in this background section, as well as aspects ofthe description which may not otherwise qualify as prior art at the timeof filing, are neither expressly or impliedly admitted as prior artagainst the present invention.

SUMMARY

The present disclosure relates to a method for controlling an autonomousvehicle that identifies a trend for an autonomous vehicle based onautonomous vehicle profiles associated with one or more vehicles withina network, identifies optimal driving conditions for the autonomousvehicle based on the trend; and controls one or more subsystems of theautonomous vehicle based on the identified optimal driving conditions.

The present disclosure also relates to a system for controlling anautonomous vehicle. The system includes a network of one or morevehicles and processing circuitry. The processing circuitry isconfigured to identify a trend for an autonomous vehicle based onautonomous vehicle profiles associated with the one or more vehicles ofthe network, identify optimal driving conditions for the autonomousvehicle based on the trend, and control one or more subsystems of theautonomous vehicle based on the identified optimal driving conditions.

The foregoing paragraphs have been provided by way of generalintroduction, and are not intended to limit the scope of the followingclaims. The described embodiments, together with further advantages,will be best understood by reference to the following detaileddescription taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 is a schematic of a system environment according to one example;

FIG. 2 is a block diagram of a system for controlling a vehicleaccording to one example;

FIG. 3 is a flowchart for a process for controlling the vehicleaccording to one example;

FIG. 4 is a block diagram of a server according to one example; and

FIG. 5 is a block diagram of a vehicle according to one example.

DETAILED DESCRIPTION

The terms “a” or “an”, as used herein, are defined as one or more thanone. The term “plurality”, as used herein, is defined as two or morethan two. The term “another”, as used herein, is defined as at least asecond or more. The terms “including” and/or “having”, as used herein,are defined as comprising (i.e., open language). The term “coupled”, asused herein, is defined as connected, although not necessarily directly,and not necessarily mechanically. The term “program” or “computerprogram” or similar terms, as used herein, is defined as a sequence ofinstructions designed for execution on a computer system. A “program”,or “computer program”, may include a subroutine, a program module, ascript, a function, a procedure, an object method, an objectimplementation, in an executable application, an applet, a servlet, asource code, an object code, a shared library/dynamic load libraryand/or other sequence of instructions designed for execution on acomputer system.

Reference throughout this document to “one embodiment”, “certainembodiments”, “an embodiment”, “an implementation”, “an example” orsimilar terms means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present disclosure. Thus, theappearances of such phrases or in various places throughout thisspecification are not necessarily all referring to the same embodiment.Furthermore, the particular features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments withoutlimitation.

The term “or” as used herein is to be interpreted as an inclusive ormeaning any one or any combination. Therefore, “A, B or C” means “any ofthe following: A; B; C; A and B; A and C; B and C; A, B and C”. Anexception to this definition will occur only when a combination ofelements, functions, steps or acts are in some way inherently mutuallyexclusive.

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout several views, the followingdescription relates to a system and associated methodology forcontrolling an autonomous vehicle. The system described herein uses bigdata and imaging techniques to create a safer driving network ofautonomous vehicles. A comprehensive profile of each autonomous vehicleis obtained and leveraged to provide the safer network of autonomousvehicles.

FIG. 1 is a schematic of a system environment for the autonomous controlsystem 100, referred to herein as system 100, according to one example.The system 100 includes a server 102, a network 104, a vehicle 106, anda database 108.

The server 102 can represent one or more servers connected to thevehicle 106 and the database 108 via the network 104. The server 102includes processing circuitry configured to perform various processesfor the system 100 including receiving data from one or more of thevehicles 106 via the network 104. Additionally, the server 102 outputscontrol signals and information to one or more of the vehicles 106 andthe database 108 via the network 104. The server 102 may control thetraffic of autonomous vehicles using cloud computing techniques. Theserver 102 identifies a trend based on data received from the vehiclesin real time. Then, the server 102 sends the control signals to thevehicle 106 to control one or more systems of the vehicle 106 based onthe trend. The server 102 may include a CPU 400 and a memory 402 asshown in FIG. 4 .

The network 104 can represent one or more networks connecting thevehicle 106, the server 102, and the database 108. Suitable networks caninclude or interface with any one or more of a local intranet, a PAN(Personal Area Network), a LAN (Local Area Network), a WAN (Wide AreaNetwork), a MAN (Metropolitan Area Network), a VPN (Virtual PrivateNetwork), or a SAN (storage area network). Furthermore, communicationsmay also include links to any of a variety of wireless networks,including WAP (Wireless Application Protocol), GPRS (General PacketRadio Service), GSM (Global system for Mobile Communication), CDMA (CodeDivision Multiple Access) or TDMA (Time Division Multiple Access),cellular phone networks, GPS (Global Positioning System), CDPD (Cellulardigit packet data), Bluetooth radio, or an IEEE 802.11 based radiofrequency.

The vehicle 106 can represent one or more vehicles 106. FIG. 1 showsvehicles 106 a, 106 b, 106 c. The vehicle 106 may be any type of vehiclesuch as a car, truck, bus, airplane, helicopter, motorcycle, train, orship. The vehicle 106 may be gas-powered, diesel powered, ethanol,electric, hybrid, or solar-powered. The vehicle 106 may be activelyoperated by a driver or may be partially or completely autonomous orself-driving.

The database 108 can represent one or more databases. For example, thedatabase 108 may include a vehicle profiles database, a geographicaldata database, or a geographical information system (GPS). The database108 stores the autonomous vehicle profiles generated and/or acquired bythe server 102. The database 108 of the system 100 may be implemented inthe memory of the server 102. In one implementation, the database 108may be a cloud based storage system.

The description herein is provided with reference to the system beinglocated and implemented external to the vehicle 106. However, it is tobe understood that the system may alternatively or additionally beimplemented within the vehicle 106.

A dynamic network of autonomous vehicles is created using big data andimaging techniques. Each vehicle is represented as a dynamic node in thedynamic network of autonomous vehicles. The autonomous vehicles interactwith other autonomous vehicles, non-autonomous vehicles, and otherobjects (i.e., sensors, traffic signs, traffic lights, emergencyvehicles, gates, tolls, parking garages, pedestrians, and the like) in aroad environment. The autonomous vehicles are connected to the server102 via the network 104. The server 102 manages communications andinteractions within the network of autonomous vehicles. Machine learningtechniques (e.g., Neural networks, neuro-fuzzy inference systems,support vector machines, deep learning, and the like) are applied ondata obtained from sensors to ascertain optimal driving conditions ofeach autonomous vehicle within the network of autonomous vehicles. Thedetermined patterns from machine learning are communicated betweenmultiple autonomous vehicles connected via the network 104 to furtherascertain, refine, and validate the optimal driving conditions of theautonomous vehicle. For example, the server 102 using machine learningdetermines patterns of the autonomous vehicles based on: vehiclestability control (VSC)/anti-lock braking system (ABS) activation,duration of VSC/ABS usage, distance between front vehicleparameterization, and vehicle-to-vehicle (V2V) and vehicle-to-center(e.g., 3G, 4G, 5G, and WiFi connected to the Internet or backbonenetwork) communications.

The vehicle 106 may include one or more cameras. A front camera of thevehicle 106 is utilized while analyzing the GPS and point of interest(POI) data for the autonomous vehicles within a preconfigured thresholdrange. Distances can be determined between the autonomous vehicles inorder to distinguish the autonomous vehicles from each other. Using dataanalytics, the server 102 may determine positions of the autonomousvehicles and changes in the positions of the autonomous vehiclesrelative to each other. The server 102 identifies distances thatcorrespond to safe distances between the autonomous vehicles (e.g.,depending on the relative speed of the two neighboring vehicles,environmental conditions, and the like) to reduce tailgating and promotesafer driving practices. The server 102 may control one or moreautonomous vehicle systems based on the identified distances for optimaldriving conditions. For example, the server 102 may output controlsignals to control the speeds of vehicles 106 a and 106 b in order tomaintain a safe distance between vehicles 106 a and 106 b. In oneimplementation, in response to determining that one or more vehicles arenon-autonomous the server 102 may monitor the speed of thenon-autonomous vehicles and continuously identify and control the speedof the autonomous vehicle such as to maintain a safe distance between anon-autonomous vehicle and an autonomous vehicle.

The response of the autonomous vehicles to road conditions can bedetermined from data acquired from sensors included in the autonomousvehicles. Data analytics on the acquired data is used to identify theimpact of road conditions (e.g., weather or construction) on theautonomous vehicle, such as whether certain road conditions preventsmooth braking for certain autonomous vehicles. For example, when a roadis very slippery due to snow or ice, machine learning accounts forfriction and reaction time of the tires to the road. The autonomousvehicle or the server 102 determines that a longer safe distance may beneeded between the vehicles 106. A setting parameter of the auto cruisemode is modified to correspond to the longer safe distance.

The server 102 obtains profiles on each autonomous vehicle within thenetwork of autonomous vehicles, for example, via V2V communications. Inother embodiments, the obtained profiles are generated by the server102. The server 102 shares profile information within the network ofautonomous vehicles via the network 104 (e.g., 3G, 4G, 5G, and WiFiconnections to the Internet or backbone network). A comprehensiveprofile for each vehicle is determined using edge computing. Thecomprehensive profiles may include VSC/ABS triggered frequency,position, and date time, outside temperatures obtained by vehiclesensors, and wiper activation conditions.

Front vehicles send information from the vehicle profile to following(i.e., trailing) vehicles. The information sent may include: vehicleweight, brake capability, tire pressure status, speed, and maximumbraking gravity set by the autonomous driving system. Furthermore, edgecomputing in the following vehicles (i.e., trailing vehicles) creates amore comprehensive profile by using: distance from the front vehicle asa numeric value; speed as a numeric value; VSC/ABS activations status;and calculated distances optimized from the front vehicles. For example,if the front vehicle is a sports vehicle that has high brakingcapability with light weight, then the target distance during autonomousdriving from the front vehicle is more than typically allotted. Targetdistances and safe distances may be stored in the database 108 for eachdriving profile. In addition, accidents associated with the targetdistances and safe distances may be uploaded to the server 102. Theserver 102 may analyze the data and update the target distanceassociated with a first road condition based on past data includingaccidents.

Not all vehicles respond the same to road conditions. The profile ofeach autonomous vehicle may be used to generate a customized response tothe road condition as described previously herein. The autonomousvehicles with the aid of machine learning may determine if furtherinformation is needed to facilitate more efficient identification orunderstanding of the effects of a roadway condition on the autonomousvehicle 106. The autonomous vehicle may poll the server 102 to obtainthe additional information when needed. For example, the autonomousvehicle may upload a license plate indicia (e.g., alpha numericcharacters) to the server 102 with a request for a vehicle type andweight. The server 102 may retrieve from the database 108 the vehicletype and weight by referencing the license plate indicia. Based on theobtained additional information, parameters in the autonomous vehicleare adjusted by, for example, indicating the following distance shouldbe 130% of the standard target distance based on the front vehicle'sweight. The weight of the vehicle may be obtained in real time using avehicle's weight sensor to account for the current weight (i.e., vehiclefully loaded, empty).

The comprehensive profiles are updated and shared continuously based ona predetermined frequency (e.g., one second, one minute, or the like)and/or based on a triggering event (e.g., change to vehicle weight dueto loading\unloading). The data contained within the comprehensiveprofiles are shared directly with other vehicles and/or upload to theserver 102. As described previously herein, the server 102 may send dataincluded in the comprehensive profiles to other vehicles. The profilesof autonomous vehicles are used to validate and refine response to roadconditions. In one implementation, the server 102 may monitor thedistance between vehicles (i.e., stopping distance). The server 102 maycompare the distance to a predetermined threshold. In response todetermining that the distance is less than the predetermined threshold,the server 102 using machine learning and data analytics may identify animproved response. The server 102 may analyze road and weatherconditions associated with the stopping distance. When a firstautonomous vehicle stops very close to a second autonomous vehicle on aslippery road, the server 102 may identify an improved response toslippery roads. VSC/traction control system (TSC) triggered informationmay be used to refine (i.e., improve) the response of the firstautonomous vehicle to the slippery roads.

Weather information may be directly obtained from a camera attached tothe autonomous vehicle 106. In addition to camera data, additionalvehicle sensor information, such as VSC/ABS status and outsidetemperature sensor data, is used to determine current weather conditionsthe autonomous vehicle is exposed to. The server 102 may also send tothe autonomous vehicle 106 weather information from a third party orother vehicles or sensors in the vicinity of the autonomous vehicle.

FIG. 2 is a block diagram of a vehicle 106 according to one example. Thevehicle 106 may include a communication module 202, a control module204, a location module 206, a sensors module 208, and a subsystemsmodule 210. The communication module 202 may be a direct communicationmodule (DCM) that provides communications over a network to any serverthat may be included with multiple services available to the vehicleuser, for example, the server 102. The control module 204 may controlone or more subsystems of the subsystems module 210. The location module206 may include positioning circuitry to determine the geographicallocation of the vehicle 106. The sensors module 208 may include one ormore cameras, a temperature sensor, a weight sensor, and the like. Forexample, the sensors module 208 may include a front camera and a rearcamera.

The modules described herein may be implemented as either softwareand/or hardware modules and may be stored in any type ofcomputer-readable medium or other computer storage device. For example,each of the modules described herein may be implemented in circuitrythat is programmable (e.g. microprocessor-based circuits) or dedicatedcircuits such as application specific integrated circuits (ASICS) orfield programmable gate arrays (FPGAS). In one embodiment, a centralprocessing unit (CPU) could execute software to perform the functionsattributable to each of the modules described herein. The CPU mayexecute software instructions written in a programming language such asJava, C, or assembly. One or more software instructions in the modulesmay be embedded in firmware, such as an erasable programmable read-onlymemory (EPROM).

FIG. 3 is a flowchart for a method 300 for controlling the vehicle 106according to one example.

At S302, the server 102 may generate an autonomous vehicle profile basedon driving data and environmental data acquired from the autonomousvehicle. In one implementation, the autonomous vehicle may generate theautonomous vehicle profile based on the driving and environmental data.The autonomous vehicle shares the autonomous vehicle profile with theserver 102 and/or other vehicles 106 via the network 104. The server 102may acquire the autonomous vehicle profiles at preset time intervals.For example, the server 102 may poll the vehicle 106 to upload theautonomous vehicle profile every 15 minutes. In other implementations,the vehicle 106 may upload the vehicle profiles at preset timeintervals.

In one implementation, a first vehicle may share the autonomous vehicleprofile (associated with the first vehicle) with vehicles within apredetermined distance when a connection to the server 102 fails (e.g.,in a tunnel).

At S304, the server 102 may identify a trend for one or more autonomousvehicles based on the autonomous vehicle profiles received from one ormore vehicles. The autonomous vehicle profiles include sensor data asdescribed previously herein. The trend may include short stoppingdistance, an increase in lane change, and the like.

At S306, the server 102 may determine optimal driving conditions basedon the trend. For example, the server 102 may identify a safe distancebetween two vehicles based on the trend. For example, in response todetermining that the vehicles are stopping at a close distance betweeneach other (e.g., below a predetermined threshold), the server 102 mayincrease the target stopping distance. In one example, the server 102may decrease the traveling speed when the vehicles are changing lanesoften to avoid obstacles.

At S308, the server 102 may send control signals to the autonomousvehicle based on the determined optimal driving conditions. For example,the server 102 may send control signals to the control module 204. Inturn, the control module 204 may modify one or more settings of thesubsystems module 210 based on the control signals.

Although the flowcharts show specific orders of executing functionallogic block, the order of executing the blocks may be changed relativeto the order shown, as will be understood by one of ordinary skill inthe art. Also, two or more blocks shown in succession may be executedconcurrently or with partial concurrence.

A system which includes the features in the foregoing descriptionprovides numerous advantages to users. In particular, safety ofautonomous vehicle networks is improved. Settings of one or moresubsystems of the autonomous vehicle are automatically adjusted based onan identified trend.

Next, a hardware description of the server 102 according to exemplaryembodiments is described with reference to FIG. 4 . In FIG. 4 , theserver 102 includes a CPU 400 which performs the processes describedherein. The process data and instructions may be stored in memory 402.These processes and instructions may also be stored on a storage mediumdisk 404 such as a hard drive (HDD) or portable storage medium or may bestored remotely. Further, the claimed advancements are not limited bythe form of the computer-readable media on which the instructions of theinventive process are stored. For example, the instructions may bestored on CDs, DVDs, in FLASH memory, RAM, ROM, PROM, EPROM, EEPROM,hard disk or any other information processing device with which thecomputer 426 communicates, such as a server or computer.

Further, the claimed advancements may be provided as a utilityapplication, background daemon, or component of an operating system, orcombination thereof, executing in conjunction with CPU 400 and anoperating system such as Microsoft® Windows®, UNIX®, Oracle® Solaris,LINUX®, Apple macOS® and other systems known to those skilled in theart.

In order to achieve the server 102, the hardware elements may berealized by various circuitry elements, known to those skilled in theart. For example, CPU 400 may be a Xenon® or Core® processor from IntelCorporation of America or an Opteron® processor from AMD of America, ormay be other processor types that would be recognized by one of ordinaryskill in the art. Alternatively, the CPU 400 may be implemented on anFPGA, ASIC, PLD or using discrete logic circuits, as one of ordinaryskill in the art would recognize. Further, CPU 400 may be implemented asmultiple processors cooperatively working in parallel to perform theinstructions of the inventive processes described above.

The server 102 in FIG. 4 also includes a network controller 406, such asan Intel Ethernet PRO network interface card from Intel Corporation ofAmerica, for interfacing with network 104. As can be appreciated, thenetwork 104 can be a public network, such as the Internet, or a privatenetwork such as LAN or WAN network, or any combination thereof and canalso include PSTN or ISDN sub-networks. The network 104 can also bewired, such as an Ethernet network, or can be wireless such as acellular network including EDGE, 3G and 4G wireless cellular systems.The wireless network can also be WiFi®, Bluetooth®, or any otherwireless form of communication that is known.

The server 102 further includes a display controller 408, such as aNVIDIA® GeForce® GTX or Quadro® graphics adaptor from NVIDIA Corporationof America for interfacing with display 410, such as a Hewlett Packard®HPL2445w LCD monitor. A general purpose I/O interface 412 interfaceswith a keyboard and/or mouse 414 as well as an optional touch screenpanel 416 on or separate from display 410. General purpose I/O interfacealso connects to a variety of peripherals 418 including printers andscanners, such as an OfficeJet® or DeskJet® from Hewlett Packard®.

The general purpose storage controller 420 connects the storage mediumdisk 404 with communication bus 422, which may be an ISA, EISA, VESA,PCI, or similar, for interconnecting all of the components of the server102. A description of the general features and functionality of thedisplay 410, keyboard and/or mouse 414, as well as the displaycontroller 408, storage controller 420, network controller 406, andgeneral purpose I/O interface 412 is omitted herein for brevity as thesefeatures are known.

FIG. 5 is a simplified block diagram of a vehicle environment 500 inwhich embodiments of the invention disclosed herein may be implemented.The vehicle environment 500 includes a vehicle 501 in communication withone or more external devices 550 by way of one or more external networks580. Vehicle 501 also includes various internal networks 540 forinterconnecting several vehicle devices within the vehicle as will bediscussed below. The vehicle environment 500 may also include one ormore in-vehicle mobile device 530. External devices 550 include anydevice located outside the vehicle 501 such that the external devicemust communicate with the vehicle and its devices by an external network580. For example, the external devices may include mobile devices,electronic devices in networked systems (e.g., servers or clients in alocal area network (LAN), etc.), on board computers of other vehiclesetc. In-vehicle mobile devices 530 are devices which are located within,or in the vicinity of the vehicle 501 such that the in-vehicle mobiledevice can communicate directly with internal networks 540 of thevehicle 501. In-vehicle mobile devices 530 may also connect withexternal networks 580.

Vehicle 501 includes vehicle devices integral with or otherwiseassociated with the vehicle 501. In the embodiment of FIG. 5 , vehicledevices include one or more sensors 503, one or more actuators 505, oneor more control units 507, one or more media systems 508, one or moredisplays 509, one or more routers 511, one or more antenna 513, and oneor more on board computers 520. As used herein, the term “vehicledevice” is meant to encompass sensors, actuators, controllers,electronic control units (ECUs), detectors, instruments, embeddeddevices, media devices including speakers, a CD and/or DVD player, aradio, vehicle navigation systems (e.g., GPS) displays, other peripheralor auxiliary devices or components associated with the vehicle 501.

Sensors 503 detect various conditions within (or in the immediatevicinity of) the vehicle 501. For example, sensors 503 may betemperature sensors, photosensors, position sensors, speed sensors,angle sensors or any other sensor for detecting a diagnostic conditionor other parameter of the vehicle 501 or its ambient environment.Sensors 503 may be passive or “dumb” sensors that provide an analogrepresentative of the sensed parameter, or so called “smart” sensorswith integrated memory and digital processing capability to analyze theparameter sensed within the sensor itself.

On-board computer 520 is a vehicle device for providing general purposecomputing functionality within the vehicle 501. The on-board computer520 typically handles computationally intensive functions based onsoftware applications or “apps” loaded into memory. On-board computer520 may also provide a common interface for different communicationnetworks in the vehicle environment 500. On-board computer 520 includesone or more processor 521, one or more memory 523, one or more userinterface 525, and one or more network interface 527.

Multiple internal vehicle networks represented by 540 may exist in thevehicle 501 to provide communication pathways to various vehicle devicesdistributed throughout the vehicle 501. An internal vehicle network 540is a collection of nodes, such as vehicle devices, integrated with orotherwise linked to the vehicle and interconnected by communicationmeans. FIG. 5 shows four examples of such hard wired networks:Controller Area Network (CAN) 541, Local Internet Network (LIN) 543,Flexray bus 545, and Media Oriented System Transport (MOST) network 547.

Other hard wired internal networks such as Ethernet may be used tointerconnect vehicle devices in the vehicle 501. Further, internalwireless networks 549, such as near field communications, Bluetooth,etc. may interconnect vehicle devices.

Users (driver or passenger) may initiate communication in vehicleenvironment 500 via some network, and such communication may beinitiated through any suitable device such as, in-vehicle mobile device530, display 509, user interface 525, or external devices 550.

Obviously, numerous modifications and variations are possible in lightof the above teachings. It is therefore to be understood that within thescope of the appended claims, the invention may be practiced otherwisethan as specifically described herein.

Thus, the foregoing discussion discloses and describes merely exemplaryembodiments of the present invention. As will be understood by thoseskilled in the art, the present invention may be embodied in otherspecific forms without departing from the spirit or essentialcharacteristics thereof. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting of the scopeof the invention, as well as other claims. The disclosure, including anyreadily discernible variants of the teachings herein, defines, in part,the scope of the foregoing claim terminology such that no inventivesubject matter is dedicated to the public.

The invention claimed is:
 1. A method for controlling an autonomousvehicle, the method comprising: identifying, using processing circuitrythat implements a neural network, a trend for the autonomous vehiclebased on an autonomous vehicle profile of the autonomous vehicle andautonomous vehicle profiles associated with one or more other vehicleswithin a network; identifying optimal driving conditions for theautonomous vehicle based on the trend; and controlling one or moresubsystems of the autonomous vehicle based on the identified optimaldriving conditions, wherein the autonomous vehicle profile of theautonomous vehicle includes at least stability control activation data.2. The method of claim 1, further comprising: acquiring driving data andenvironmental data from the autonomous vehicle; and determining theautonomous vehicle profile for the autonomous vehicle based on thedriving data and the environmental data.
 3. The method of claim 1,further comprising: polling the autonomous vehicle to upload anautonomous vehicle profile thereof at preset time intervals.
 4. Themethod of claim 1, further comprising: receiving from the autonomousvehicle a request for additional information; retrieving the requestedadditional information from a database; and outputting the requestedadditional information to the autonomous vehicle.
 5. The method of claim4, wherein the request includes a license plate indicia.
 6. The methodof claim 1, wherein the autonomous vehicle profile of the autonomousvehicle further includes at least one of braking system activation data,distance between front vehicle parameterization, and environmentalconditions.
 7. The method of claim 1, wherein determining the optimaldriving conditions includes: identifying a target distance between twoautonomous vehicles.
 8. The method of claim 1, further comprising:acquiring from a first vehicle data including at least one of a vehiclespeed, a vehicle weight, a tire pressure status, and a maximum brakinggravity; and outputting to a second vehicle the data, wherein the secondvehicle is trailing the first vehicle.
 9. The method of claim 8, furthercomprising: analyzing the data; and increasing a target distance betweenthe first vehicle and the second vehicle when the data indicates thatthe first vehicle has a high braking capability.
 10. A system forcontrolling an autonomous vehicle, the system comprising: a networkconnecting one or more vehicles; and processing circuitry configured toidentify, using a neural network, a trend for the autonomous vehiclebased on an autonomous vehicle profile of the autonomous vehicle andautonomous vehicle profiles associated with the one or more vehicles,identify optimal driving conditions for the autonomous vehicle based onthe trend, and control one or more subsystems of the autonomous vehiclebased on the identified optimal driving conditions, wherein theautonomous vehicle profile of the autonomous vehicle includes at leaststability control activation data.
 11. The system of claim 10, whereinthe processing circuitry is further configured to: acquire driving dataand environmental data from the autonomous vehicle; and determine theautonomous vehicle profile of the autonomous vehicle based on thedriving data and the environmental data.
 12. The system of claim 10,wherein the processing circuitry is further configured to: poll theautonomous vehicle to upload an autonomous vehicle profile thereof atpreset time intervals.
 13. The system of claim 10, wherein theprocessing circuitry is further configured to: receive from theautonomous vehicle a request for additional information; retrieve therequested additional information from a database; and output therequested additional information.
 14. The system of claim 13, whereinthe request includes a license plate indicia.
 15. The system of claim10, wherein the autonomous vehicle profile of the autonomous vehiclefurther includes at least one of braking system activation data,distance between front vehicle parameterization, and environmentalconditions.
 16. The system of claim 10, wherein the processing circuitryis further configured to: identify a target distance between twoautonomous vehicles.
 17. The system of claim 10, wherein the processingcircuitry is further configured to: acquire from a first vehicle dataincluding at least one of a vehicle speed, a vehicle weight, a tirepressure status, and a maximum braking gravity; and output to a secondvehicle the data, wherein the second vehicle is trailing the firstvehicle.
 18. A non-transitory computer readable medium storingcomputer-readable instructions therein which when executed by a computercause the computer to perform a method for controlling an autonomousvehicle, the method comprising: identifying, using a neural network, atrend for the autonomous vehicle based on an autonomous vehicle profileof the autonomous vehicle and on autonomous vehicle profiles associatedwith one or more vehicles within a network; identifying optimal drivingconditions for the autonomous vehicle based on the trend; andcontrolling one or more subsystems of the autonomous vehicle based onthe identified optimal driving conditions, wherein the autonomousvehicle profile of the autonomous vehicle includes at least stabilitycontrol activation data.